A theoretical model to predict performance of integrated robotic systems

IF 9.1 1区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Z.M. Bi, A. Mikkola, H. Handroos, C. Luo
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引用次数: 0

Abstract

With modularized architecture, integrated solutions can be configured by selecting and assembling a set of selected off-the-shelf functional modules to satisfy users’ needs optimally. While the attributes and properties of these modules are validated at components levels, the performances of system can be affected greatly by integration and interactions. Existing methodologies on system integration focus on system architecture, hardware and software reuses, communications, interfaces, and interoperation. There is the need to develop effective verification and validation (V&V) methods to assure the first-time-right from a virtual model to physical model in terms of the composability of system components to predict the performance of an integrated systems; note that not all attributes of composability can be verified by self-adaptability of cyber-physical systems. In this paper, we will focus on V&V of integrated robotic systems, and we will explore the relations of an integrated system with its components in terms of some performance criteria including functionalities, responsiveness, accuracy, and repeatability. The problem itself is newly formulated, and it is crucial for designers to predict and optimize system performance based on the selection and assemblage of system modules. The work in this paper opens new field of research in standardizing verification and validation process in designing collaborative robot systems
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来源期刊
Robotics and Computer-integrated Manufacturing
Robotics and Computer-integrated Manufacturing 工程技术-工程:制造
CiteScore
24.10
自引率
13.50%
发文量
160
审稿时长
50 days
期刊介绍: The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.
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